Open Access

ARTICLE

Transient Stability Assessment Model and Its Updating Based on Dual-Tower Transformer

Nan Li^1,2,*, Jingxiong Dong², Liang Tao³, Liang Huang³
1 Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, 132012, China
2 School of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China
3 State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, 136000, China
* Corresponding Author: Nan Li. Email:
(This article belongs to the Special Issue: Emerging Technologies for Future Smart Grids)

Energy Engineering https://doi.org/10.32604/ee.2025.062667

Received 24 December 2024; Accepted 20 March 2025; Published online 15 April 2025

Abstract

With the continuous expansion and increasing complexity of power system scales, the binary classification for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation. Therefore, this paper proposes a multi-class transient stability assessment model based on an improved Transformer. The model is designed with a dual-tower encoder structure: one encoder focuses on the time dependency of data, while the other focuses on the dynamic correlations between variables. Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction process, thereby enhancing the accuracy of multi-class evaluation in power systems. Additionally, this paper introduces a hybrid sampling strategy based on sample boundaries, which addresses the issue of sample imbalance by increasing the number of boundary samples in the minority class and reducing the number of non-boundary samples in the majority class. Considering the frequent changes in power grid topology or operation modes, this paper proposes a two-stage updating scheme based on self-supervised learning: In the first stage, self-supervised learning is employed to mine the structural information from unlabeled data in the target domain, enhancing the model’s generalization capability in new scenarios. In the second stage, a sample screening mechanism is used to select key samples, which are labeled through long-term simulation techniques for fine-tuning the model parameters. This allows for rapid model updates without relying on many labeled samples. This paper’s proposed model and update scheme have been simulated and verified on two node systems, the IEEE New England 10-machine 39-bus system and the IEEE 47-machine 140-bus system, demonstrating their effectiveness and reliability.

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Keywords

Transient stability assessment; sample imbalance; dual-tower transformer network; self-supervised learning

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